Guidance on estimation of wild boar population abundance and density: methods, challenges, possibilities

Abundance, camera trap, density, hunting bag, monitoring, population estimation, Sus scrofa, wild boar
First published in EFSA Supporting Publications
11 July 2018
3 July 2018
External Scientific Report

The aim of this guidance is to assess the accuracy and reliability of the methods for estimation of density (i.e. population size per area unit) and relative abundance (i.e. relative representation of a species in a particular ecosystem, a kind of proxy of the density) of wild boar and to provide indications for calculating reliable and accurate estimates of those parameters using comparable methods. For this purpose eighteen methods were reviewed and evaluated. Since counting wild boar on a large regional scale is unfeasible, estimations of density and abundance are reliable only at local scale in specific habitats. Three methods (camera trapping, drive counts, and distance sampling with thermography) were recommended to estimate wild boar density on a local scale, and guidelines for their implementation was provided. In particular camera trapping is a method that can be conducted everywhere, irrespective of the habitat specificities and at any time to generate comparable data. Wild boar demographic data obtained by different methods cannot directly be combined by simple equations but spatial models are needed to determine abundance and predicted densities that are reliable at larger scales. On a large spatial scale and to describe long‐term trends, high quality hunting data statistics (collected on a fine spatial scale) have the highest availability and potential comparability potential across Europe, and these can be used in predictive spatial modelling of wild boar relative abundance and density. There is need for compiling and validating wild boar abundance data at different spatial scales: hunting bag data alone are not sufficient because a calibration with more accurate density estimation methods conducted at local scale is required. The latter are also required for evaluating predictive models for large areas and converting predicted relative abundances into densities.

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